Earnings forecasts of female CEOs: quality and consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study examines the voluntary disclosure of earnings forecasts by female CEOs. We find that in the backdrop of increased pressure to perform from investors and other stakeholders, female CEOs tend to issue more earnings forecasts than male CEOs, and those forecasts are more accurate. We also find that while financial analysts generally prefer to follow companies headed by male CEOs, female CEOs’ efforts to issue accurate earnings forecasts pay off, as these efforts help them close the analyst coverage gap. We provide complementary evidence on the disclosure efforts of female CEOs with regard to updates to the forecast and the 10-K report. Lastly, we show that financial analysts rely more on the earnings forecasts of female CEOs, possibly because they recognize female CEOs’ superior forecasting quality. Our results are robust to the use of alternative research designs, including difference-in-difference, propensity score matching, and entropy balancing. Overall, our study documents gender differences in voluntary disclosure by senior management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it